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A detection method for low-pixel ratio object

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Abstract

Low-pixel object detection is a kind of difficult program. Existing object detection benchmarks and methods mainly focus on standard detection task. However, these way cannot get good performance on low-pixel ratio object detection, which has a few pixel in high resolution images. In order to solve it, we propose a new deep learning framework. This framework improves Faster R-CNN by combining multiple level feature map and optimizing anchor size for bounding box recognition. In order to validate our approach, we collect and annotate a dataset for road garbage detection, which contains 801 images and 966 bounding boxes. Experiments demonstrate that our framework outperforms other state-of-the-art detection methods. What’s more, our method can apply on road garbage target.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61671423 and Grant No. 61271403.

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Authors and Affiliations

  1. School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230027, China

    Rui Zhang, Dong Yin, Jinwen Ding, Yuhao Luo, Mingyue Yuan, Chengfeng Zhu & Zhipeng Zhou

  2. Key Laboratory of Electromagnetic Space Information of CAS, Hefei, Anhui, 230027, China

    Rui Zhang, Dong Yin, Yuhao Luo, Mingyue Yuan, Chengfeng Zhu & Zhipeng Zhou

  3. Department of Material Science and Engineering, USTC, Hefei, Anhui, 230026, China

    Jinwen Ding

  4. Anhui CAS-Zhonghuan Defence Equipment Technology Co., LTD, Hefei, Anhui, 230001, China

    Wei Liu

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  1. Rui Zhang
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  2. Dong Yin
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  3. Jinwen Ding
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  4. Yuhao Luo
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Correspondence to Dong Yin.

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Zhang, R., Yin, D., Ding, J. et al. A detection method for low-pixel ratio object. Multimed Tools Appl 78, 11655–11674 (2019). https://doi.org/10.1007/s11042-018-6653-6

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  • DOI: https://doi.org/10.1007/s11042-018-6653-6

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